Advanced Network Intrusion Detection Using Hybrid Anomaly Detection and Intelligent Alert Classification

Abstract

The rapid expansion of networked systems has significantly increased the frequency and sophistication of cyber-attacks, making network intrusion detection a critical component of cybersecurity. Traditional intrusion detection systems, particularly signature-based approaches and single-model machine learning classifiers, often fail to detect unknown or zero-day attacks and are prone to high false-positive rates, limiting their practical effectiveness. Existing methods heavily rely on predefined attack patterns and lack mechanisms to prioritize alerts, leading to operational inefficiency and alert fatigue. To address these limitations, this paper proposes an advanced network intrusion detection framework that integrates hybrid anomaly detection with intelligent alert classification. The framework leverages the publicly available NSL-KDD dataset, which contains diverse network traffic features and labeled attack types, enabling both temporal and statistical analysis. An (LSTM)-Autoencoder models normal traffic sequences to identify temporal anomalies, while an Isolation Forest captures rare point anomalies. Detected anomalies are then fed into an XGBoost-based module for alert classification and prioritization, generating actionable insights for security operations. The novelty of this approach lies in its hybrid design that combines deep learning (DL) and unsupervised anomaly detection to enhance robustness, minimize false positives, and provide meaningful alert prioritization. Experimental evaluation on the NSL-KDD dataset demonstrates superior performance compared to conventional signature-based Intrusion Detection System (IDS) and standalone Machine Learning (ML) models, offering improved detection of both known and unknown attacks. This framework advances AI- based IDS by improving reliability, adaptability, and operational efficiency.